Update module.py

module.py

@@ -1,5 +1,4 @@
 """
-
 Goal: Read in raw Edgar files, downloadable from the
 ...
 
@@ -8,10 +7,94 @@
 
 See Example.py for implementation of the EdgarAnalyzer functions
 """
+import os
+import re
+import unicodedata
+from bs4 import BeautifulSoup
+import glob
+import pandas as pd
 
 class EdgarAnalyzer(object):
     """
     Object class to read in Edgar 10k Data
-
     """
+    def clean_html_content(html_content):
+       # Parse the HTML content
+        try:
+            soup = BeautifulSoup(html_content, "html.parser")
+        except Exception as e:
+            print(f"html.parser failed: {e}")
+            try:
+                soup = BeautifulSoup(html_content, "lxml")
+            except Exception as e:
+                print(f"lxml failed: {e}")
+                try:
+                    soup = BeautifulSoup(html_content, "html5lib")
+                except Exception as e:
+                    print(f"html5lib failed: {e}")
+                    raise
+        # soup = BeautifulSoup(html_content, "html.parser")
+
+        # Removes all tags
+        for tag in soup.find_all(True):
+            tag.unwrap()
+
+        # Extract text and normalize Unicode characters
+        text = soup.get_text(separator=' ')
+        text = unicodedata.normalize('NFKD', text)
+
+        # Remove any remaining HTML entities
+        text = re.sub(r'<.*?>', '', text)  # Remove any remaining HTML tags
+        text = re.sub(r'&[a-zA-Z0-9#]+;', ' ', text)
+
+        # Define a function to remove gibberish based on specific patterns
+        def remove_gibberish(text):
+            # Removes long sequences of characters without spaces
+            # this will remove long normal words, e.g. characteristics
+            # text = re.sub(r'\b\w{15,}\b', '', text)
+
+            # Removes sequences with high special character density
+            text = re.sub(r'[!@#$%^&*()_+={}\[\]:;"\'<>,.?/\\|`~\-]{5,}', '', text)
+
+            # Removes lines that are mostly numbers or symbols
+            text = re.sub(r'^[^a-zA-Z\s]*$', '', text, flags=re.MULTILINE)
+
+            # Additional patterns for gibberish removal
+            # Removes base64 encoded text patterns
+            text = re.sub(r'(begin [0-9]{3} [^\n]+\n(.*\n)+end)', '', text, flags=re.MULTILINE)
+
+            # Removes lines that contain too many non-alphanumeric characters
+            text = re.sub(r'^[^\w\s]{10,}$', '', text, flags=re.MULTILINE)
+
+            return text
+
+
+        def clean_noisy_text(text):
+            # Split the text into individual words
+            words = text.split()
+
+            # Define a function to identify "noisy strings"
+            def is_noisy(word):
+                # If the word is longer than 15 characters and contains:
+                # - Mixed uppercase and lowercase letters
+                # - Numbers
+                if len(word) > 15 and (
+                    re.search(r'[A-Z]', word) and re.search(r'[a-z]', word) and re.search(r'\d', word)
+                ):
+                    return True
+                # If the word is longer than 15 characters and contains symbols
+                if len(word) > 15 and re.search(r'[^A-Za-z0-9]', word):
+                    return True
+                return False
+
+            # Keep only meaningful words, removing the noisy ones
+            cleaned_words = [word for word in words if not is_noisy(word)]
+
+            # Return the cleaned text
+            return ' '.join(cleaned_words)
+
+        text = remove_gibberish(text)
+        text = clean_noisy_text(text)
+        text = ' '.join(text.split())
 
+        return text